Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge

• URL: http://arxiv.org/abs/2102.00924v1
• Date: Mon, 1 Feb 2021 15:48:36 GMT
• Title: Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge
• Authors: Yida Xin, Henry Lieberman and Peter Chin
• Abstract summary: We argue that explicit commonsense knowledge bases can provide an essential ingredient for making good attachment decisions. Our results suggest that the commonsense knowledge-based approach can provide the best of both worlds, integrating rule-based and statistical techniques.
• Score: 1.0312968200748118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We revisit the challenging problem of resolving prepositional-phrase (PP) attachment ambiguity. To date, proposed solutions are either rule-based, where explicit grammar rules direct how to resolve ambiguities; or statistical, where the decision is learned from a corpus of labeled examples. We argue that explicit commonsense knowledge bases can provide an essential ingredient for making good attachment decisions. We implemented a module, named Patch-Comm, that can be used by a variety of conventional parsers, to make attachment decisions. Where the commonsense KB does not provide direct answers, we fall back on a more general system that infers "out-of-knowledge-base" assertions in a manner similar to the way some NLP systems handle out-of-vocabulary words. Our results suggest that the commonsense knowledge-based approach can provide the best of both worlds, integrating rule-based and statistical techniques. As the field is increasingly coming to recognize the importance of explainability in AI, a commonsense approach can enable NLP developers to better understand the behavior of systems, and facilitate natural dialogues with end users.

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